Face recognition plays an important role in many robotic and human–computer interaction systems. To this end, in recent years, sparse-representation-based classification and its variants have drawn extensive attention in compress sensing and pattern recognition. For image classification, one key to the success of a sparse-representation-based approach is to extract consistent image feature representations for the images of the same subject captured under a wide spectrum of appearance variations, for example, in pose, expression and illumination. These variations can be categorized into two main types: geometric and textural variations. To eliminate the difficulties posed by different appearance variations, the article presents a new collaborative-representation-based face classification approach using deep aligned neural network features. To be more specific, we first apply a facial landmark detection network to an input face image to obtain its fine-grained geometric information in the form of a set of 2D facial landmarks. These facial landmarks are then used to perform 2D geometric alignment across different face images. Second, we apply a deep neural network for facial image feature extraction due to the robustness of deep image features to a variety of appearance variations. We use the term deep aligned features for this two-step feature extraction approach. Last, a new collaborative-representation-based classification method is used to perform face classification. Specifically, we propose a group dictionary selection method for representation-based face classification to further boost the performance and reduce the uncertainty in decision-making. Experimental results obtained on several facial landmark detection and face classification data sets validate the effectiveness of the proposed method.

中文翻译：

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具有组字典选择的基于协作表示的人脸分类的深度对齐特征提取

人脸识别在许多机器人和人机交互系统中发挥着重要作用。为此，近年来，基于稀疏表示的分类及其变体在压缩感知和模式识别中引起了广泛关注。对于图像分类，基于稀疏表示的方法成功的一个关键是为在广泛的外观变化（例如姿势、表情和光照）下捕获的同一对象的图像提取一致的图像特征表示。这些变化可以分为两种主要类型：几何变化和纹理变化。为了消除不同外观变化带来的困难，本文提出了一种使用深度对齐的神经网络特征的新的基于协作表示的人脸分类方法。更具体地说，我们首先将面部标志检测网络应用于输入面部图像，以一组 2D 面部标志的形式获取其细粒度的几何信息。这些面部标志然后用于在不同的面部图像之间执行 2D 几何对齐。其次，由于深度图像特征对各种外观变化的鲁棒性，我们将深度神经网络应用于面部图像特征提取。对于这种两步特征提取方法，我们使用术语“深度对齐特征”。最后，一种新的基于协作表示的分类方法用于执行人脸分类。具体来说，我们提出了一种基于表示的人脸分类的组字典选择方法，以进一步提高性能并减少决策中的不确定性。